Labels you can
actually train on.
Bounding boxes, polygons, keypoints, masks, OCR, medical and 3D — annotated by certified pods with two-pass review and continuous gold-task calibration.
Built for production, not just demos.
- 2D: BBox, polygon, keypoint, semantic + instance segmentation
- 3D: LiDAR / point-cloud cuboids and segmentation
- Medical: DICOM with board-certified reviewers (radiology, pathology)
- OCR + document understanding (forms, tables, KIE)
- Geospatial: building footprints, road networks, change detection
- Two-pass annotation + adjudicator on disagreement
- Gold-task injection every batch for calibration
- Active-learning sampling with embedding similarity
How a typical engagement runs.
Schema
Lock the ontology, edge-case examples and per-class rules with your ML team.
Calibrate
Pod completes 100 gold tasks; we adjust guidelines and re-train until IAA passes.
Pilot
1k-record pilot batch with full per-annotator metrics + confusion matrix.
Production
Scale to weekly batches with live IAA dashboards and adjudication queue.
Iterate
Continuous schema refinement based on model performance + edge cases.
What you get in your bucket.
Questions, answered.
Can you handle medical imaging?
Yes — board-certified radiologists, pathologists and ophthalmologists annotate DICOM/NIfTI/WSI. HIPAA-grade pods with BAA in place.
What's your quality methodology?
Two independent annotators per record + adjudicator on disagreement + gold-task calibration every batch. We publish per-batch IAA and a confusion matrix.
Do you support active learning?
Yes — we integrate with your model to surface low-confidence and high-disagreement samples, plus embedding-based diversity sampling.
Which annotation tools do you use?
Tool-agnostic. We work in CVAT, Label Studio, V7, Encord, Scale Studio, and our own hosted studio — or your private deployment.
Ready to build
AI you can trust?
Talk to a solutions architect — get a pilot scoped in 48 hours.